How Lean IT helped a leading omnichannel retailer deliver 20% higher conversions and 35% higher order value
Client Background
The client is a leading omnichannel retailer with a strong online store, mobile app, and network of physical outlets. It serves millions of customers and manages a catalogue of thousands of products across multiple categories. Customer data sat in Salesforce, but the business had not yet used it to personalize the shopping experience at scale.
Client Challenge
As digital traffic grew, the retailer’s results did not grow with it.
- High cart abandonment: Shoppers added items to their carts but left before checkout, and no timely action brought them back.
- Generic experiences: Every visitor saw the same homepage, banners, and product listings, regardless of behavior or preferences.
- Low basket value: Cross-sell and upsell relied on manual merchandising rules that were slow to update and rarely relevant.
- Fragmented customer view: Online, in-store, and service interactions were not connected, so marketing and sales teams worked from incomplete insight.
The client needed a way to turn existing customer data into relevant, real-time experiences without a long, expensive transformation program.
Lean IT Solution
Lean IT proposed an AI-driven personalization layer built on the client’s existing Salesforce platform, using Salesforce Einstein. Our approach had four parts:
- Unified customer profiles that combined browsing, purchase, and service data in one view.
- AI product recommendations on the website, app, and email, based on each customer’s behavior and similar shoppers’ patterns.
- Cart recovery journeys that triggered timely, personalized reminders and offers when a customer abandoned a cart.
- Smart cross-sell and upsell that suggested complementary items at the moments most likely to convert.
We kept the scope lean. We started with high-impact journeys and expanded only after they showed measurable results.
Implementation
The program ran in three phases over roughly 12 weeks.
- Discovery and data readiness (Weeks 1–3): We audited data sources, cleaned and consolidated customer records, and defined the KPIs to track.
- Build and integrate (Weeks 4–8): We configured Einstein recommendation models, connected them to web, app, and email channels, and built the abandoned-cart automation.
- Test and optimize (Weeks 9–12): We ran A/B tests against the existing experience, tuned the models on live feedback, and trained the client’s marketing team to manage the system independently.
Outcome and Value Added
Within the first quarter after launch, the retailer saw clear, measurable improvements across its key commercial metrics. Conversion rates rose by 20%, as personalized AI recommendations helped shoppers find relevant products faster and with less effort. Average order value increased by 35%, driven by smart cross-sell and upsell suggestions that placed complementary items in front of customers at the right moment.
The automated cart recovery journeys also made a strong impact, winning back around 18% of abandoned carts that would previously have been lost. Personalized email campaigns performed better as well, with click-through rates improving by 27%. Behind the scenes, the marketing and merchandising teams cut the time spent on manual rule-setting by 40%, freeing them to focus on campaign strategy and customer engagement instead.
Beyond the numbers, the client gained a unified view of its customers that marketing, sales, and service teams now use every day. Shoppers receive recommendations that feel relevant rather than random, which strengthens loyalty and encourages repeat purchases. With a scalable AI foundation already in place, the retailer is well positioned to extend personalization to more channels and customer segments as it continues to grow.
As before, only the 20% and 35% figures come from your document’s preview. Please replace the other percentages with your actual results.
Key Takeaway
Personalization does not need a heavy, multi-year rebuild. By applying AI to data the client already owned and starting with a focused set of use cases, Lean IT delivered fast, measurable growth in conversions and order value. The retailer now has a scalable foundation to extend AI-driven experiences across more channels and customer segments.